CN112615367A - Optimized scheduling method for comprehensive energy system in power Internet of things environment - Google Patents
Optimized scheduling method for comprehensive energy system in power Internet of things environment Download PDFInfo
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Abstract
The invention discloses an optimized scheduling method of a comprehensive energy system under the background of an electric power Internet of things. The comprehensive energy resource optimization scheduling method based on the comprehensive energy resource optimization comprises the steps of firstly analyzing a basic comprehensive energy resource scheduling architecture in the background of the ubiquitous power Internet of things, establishing a comprehensive energy resource system model in the background of the ubiquitous power Internet of things from the aspects of loads, networks and key energy resource equipment, further considering cloud-side architecture, information acquisition characteristics and market environment in the environment of the power Internet of things, and providing a comprehensive energy resource optimization scheduling method considering source-load interaction and multi-energy complementation based on the frequent interaction process of a heat supply network and a power grid of the comprehensive energy resource system. Aiming at the optimized scheduling method, the scheduling model solving method based on the improved particle swarm optimization is provided by considering the flexible corresponding characteristics of the load side. The method has the advantages that the scheduling of the whole comprehensive energy system and the scheduling of the load side are separately carried out by simulating the power utilization behavior of the user, so that the flexible interaction of the multi-energy complementation and the source load is realized.
Description
Technical Field
The invention relates to the field of electrical information, in particular to an optimized scheduling method of a comprehensive energy system in an electric power Internet of things environment.
Background
In order to relieve the energy pressure, on one hand, the proportion of green clean energy is increased, and on the other hand, the primary energy utilization efficiency is improved in the production and scheduling process, so that unnecessary energy waste is reduced. Therefore, the existing mode of independent planning, independent design and independent operation of each original energy supply system is broken through, integrated planning design and operation optimization of the social energy system are carried out, and finally, a unified social comprehensive energy supply and utilization system is constructed, so that the gradual utilization of primary energy and the unified planning and scheduling of secondary energy such as electric energy, heat energy and the like are realized.
The ubiquitous power internet of things is around each link of a power system, and the modern information technology and the advanced communication technology are fully applied to realize the mutual connection and the man-machine interaction of all links of the power system. Along with the construction of the ubiquitous power internet of things, the perception capability of the comprehensive energy scheduling system to the load is gradually enhanced, information exchange among all components in the system is more frequent, and the optimal scheduling method and structure of the system are changed.
In recent years, research on optimal scheduling of an integrated energy system mainly focuses on operation control of a thermocouple element to achieve optimal economic operation and increase green clean energy consumption. At present, the traditional integrated energy system scheduling is generally performed according to a centralized-parallel architecture, that is, energy coupling elements at part of nodes are considered while electric, thermal and air networks are respectively optimized and scheduled, information and energy interaction between different networks is realized through the coupling elements, and finally, an overall optimal scheduling scheme is obtained.
However, with the construction of the ubiquitous power internet of things, the traditional scheduling method of the centralized-parallel architecture faces the following two problems: (1) the ubiquitous power internet of things construction can greatly improve the sensing capability of a dispatching center on lower-layer loads, information measuring points, information collecting frequency, information collecting types and data quality are greatly increased, the quantity of measured data on a load side is rapidly increased, and if a traditional centralized-parallel dispatching framework is still adopted, the calculation speed of the dispatching center is greatly reduced due to the increase of data and the unsmooth information transmission; (2) due to the popularization of the intelligent electric meter with the function of combining multiple meters into one meter, the problem that electricity, heat, gas and other energy sources in a traditional comprehensive energy measuring system are respectively measured and data is not communicated is solved, coupling among different energy networks is not limited to large energy coupling elements any more, information exchange and energy transmission of the different energy networks can be achieved on a load side, coupling among the energy networks is tighter, if flexibility of the load side can be fully exerted, multi-energy complementation and flexible scheduling of the load side are achieved, peak clipping and valley filling of the comprehensive energy system can be facilitated, source consumption of green and clean energy can be increased, and the traditional scheduling framework cannot support the energy networks to conduct frequent information and energy interaction on the load side.
Although much research has been conducted on the optimization scheduling of the comprehensive energy system, with the construction of ubiquitous power internet of things, the traditional comprehensive energy scheduling method is difficult to fully utilize mass data on the load side, and is also difficult to support frequent information exchange and energy transfer of different energy networks such as electricity, heat and gas on the load side. By analyzing the comprehensive energy network under the background of the ubiquitous power Internet of things, the optimization scheduling architecture of the traditional comprehensive energy system is considered to be improved, and the scheduling of the whole comprehensive energy system and the scheduling of the load side are separately performed through the layered scheduling architecture, so that the flexible interaction of multi-energy complementation and source load is realized.
Disclosure of Invention
In order to solve the existing problems, the invention provides an optimized scheduling method of an integrated energy system in the context of an electric power internet of things, which is applied to the day-ahead scheduling of network source coordination of the integrated energy system, combines a brand-new scheduling architecture based on cloud-edge coordination, an information interaction mode in the context of the electric power internet of things and an optimized scheduling model solving method based on an improved particle swarm algorithm, and interconnects and fully utilizes the cloud management edge-end architecture and the multi-energy information at the load side so as to improve the energy utilization efficiency, reduce the operation cost and realize the global optimal operation of the whole area.
An optimized scheduling method for a comprehensive energy system in the context of an electric power Internet of things comprises the following steps:
s11: establishing a key equipment model and a load demand response model of the comprehensive energy system, wherein the key equipment comprises an electric energy storage equipment model and a cogeneration unit model, and the load demand response model comprises two demand response models of a heat load and an electric load;
s12: establishing a comprehensive energy system network model, wherein the comprehensive energy network comprises a power network and a heat network, and analyzing energy flow rules in different networks;
s13: establishing an optimized dispatching model of an objective function with the lowest operation cost of the comprehensive energy system as a target, wherein the operation cost of equipment is calculated according to the key equipment model in the step S11, and the income of selling electricity and selling heat is calculated through the load demand response model in the step S11;
s14: constraining the optimized operation process of the comprehensive energy system, establishing temperature constraint, load demand response constraint, generator set and heat source operation constraint, rotation standby constraint and electric energy storage equipment constraint in a heat load building according to the key equipment and load demand response model in the step S11, and establishing thermoelectric power flow constraint and power grid power flow constraint according to the comprehensive energy system network model in the step S12;
s15: and solving the optimized dispatching model based on an improved particle swarm algorithm, dividing the optimized dispatching process into an upper layer and a lower layer, wherein the upper layer is the optimized dispatching model of the comprehensive energy system in the step S13 and the step S14, the lower layer is the process of solving the energy demand of the user through the load side demand response model in the step S11, the process of calculating the objective function in the step S13 is carried out, and the optimal day-ahead dispatching result is obtained through continuous interaction of the two layers.
The key equipment model of the integrated energy system established in the step S11, wherein the model of the cogeneration unit is as follows:
when the cogeneration unit operates in the maximum output state, the relationship curve is expressed as follows:
wherein Z is a coefficient describing the relationship between the heat production and the electricity production of the turboset CHP1, etaeThe electric energy conversion efficiency of the unit under the maximum continuous working condition full-condensation mode is obtained; fin(MW) is the turboset fuel consumption rate;
when the cogeneration unit works in a variable output state, the relationship curve is expressed as follows:
in the formula, cmIs the thermoelectric ratio of the unit.
The key equipment model of the integrated energy system established in step S11, wherein the electrical energy storage equipment model is as follows:
in the formula (I), the compound is shown in the specification,
the SOC value of the electric energy storage equipment at the node i in the period t;
an energy storage efficiency for the energy storage device;
charging efficiency for the energy storage device;
and
the charging and discharging power of the energy storage equipment;
the heat supply network generally stores energy through a heat storage boiler, converts electric energy into heat energy for storage in a time period with low electricity price, and releases the heat energy in a time period with high heat demand, and the SOC of the heat supply network is calculated by the following formula:
in the formula (II)
The SOC value of the heat storage boiler at the node i in the t period is obtained;
the energy storage efficiency of the heat storage boiler is improved;
the energy charging efficiency of the heat storage boiler is improved;
and
the energy charging and discharging power of the heat storage boiler.
In the load demand response model established in step S11, the corresponding sensitivity of the load to price is generally expressed by the elastic coefficient, and the heat load for heating can be increased or decreased without excessively affecting the indoor temperature, but generally will not shift to other periods, and is generally expressed by the self-elastic coefficient:
in the formula (I), the compound is shown in the specification,
the coefficient of self-elasticity for the thermal load at node i during time t;
the heat supply price in the t period;
the thermal load at node i is t periods before the demand response;
a heat load demand response price for a period of t;
is the amount of change in thermal load after demand response;
the thermal load change is represented by the following formula:
in the formula (I), the compound is shown in the specification,
the thermal load at node i for time period t;
part of the electrical load can be transferred to other time periods, and part of the electrical load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are commonly expressed as follows:
in the formula (I), the compound is shown in the specification,
the self-elastic coefficient of the electrical load at the node i in the period t;
electricity prices for a period of t;
an electrical load at node i for a period t before a demand response;
responding price for electric load demand in t period;
the variation of the electrical load at the node i in the period t of the demand response;
the mutual elastic coefficient of the electrical load at the node i in the t period to the tau period;
the electrical load variation can be represented by the following equation:
in the formula (I), the compound is shown in the specification,
the electrical load at node i is time period t.
Further, the objective function calculation formula established in step S13 is as follows:
in the formula, CupperA scheduling cost for the scheduling; t is tdIs the number of scheduling periods; n is a radical ofsourceThe method comprises the steps of collecting all energy equipment nodes of a system; n is a radical ofLIs a load node set; n is a radical ofreThe method comprises the steps of (1) setting a renewable energy source electric field node set;
operating cost of energy equipment such as a cogeneration unit, a single generator set, a heat source and the like at the ith node in a time period t;
demand response compensation paid to the i-node load for the dispatch center at time t;
the cost of abandoning the wind and light for the node i at the time t,
the electricity purchasing cost from the regional comprehensive energy network to a superior power grid;
the cost of the generator set or heat source is represented by a quadratic model:
in the formula (I), the compound is shown in the specification,
the operating cost of the generator set or the heat source at the node i in the time period t;
and
solving the electric output and the thermal output of the generator set or the heat source at the node i through the key equipment model in the step S11 and the comprehensive energy system network model in the step S12; c. C0,i、c1,i、c2,i、c3,i、c4,iAnd c5,iCalculating a coefficient for the cost, which is determined by the self condition of the unit;
the demand response compensation for load payments is expressed as:
in the formula (I), the compound is shown in the specification,
the price is compensated for the electricity and heat demand response, and the adjustment on the load side can be realized by adjusting the compensation price; the variation of the electric energy demand and the heat energy demand is solved through a load demand response model in the step S11;
wind and light rejection costs are expressed as:
in the formula, ρabanPunishment price for wind and light abandonment,
the wind and light abandoning amount of the renewable energy source unit is reduced;
the cost of electricity purchase to the upper-level grid can be expressed as:
in the formula (I), the compound is shown in the specification,
for the purchase price of the system to the upper level grid during the period t,
and the system purchases the electric quantity from the superior power grid for the time period t.
Further, in the step S14
(1) The temperature constraint in the thermal load building is expressed on the premise that the temperature in the building is not influenced excessively and is represented as follows:
in the formula (I), the compound is shown in the specification,
and
respectively an upper limit and a lower limit of the indoor temperature after heat supply;
(2) the load demand response constraint is that the demand response of the load to the price signal has a certain limit, which is specifically expressed as:
in the formula (I), the compound is shown in the specification,
and
respectively responding to the load participation coefficients of the thermoelectric demand of the load at the node i;
the load at the i node is required for the heat energy and the electric energy at the t time period before the demand response;
(3) the output range of the generator set and the heat source is certain in the operation restriction of the generator set and the heat source, and meanwhile, the power climbing restriction of the generator is also considered, and the method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
and
respectively the output of a power supply or a heat source at a node i in the t time period; pi min，Pi max，
Maximum minimum generated heat production power, P, of power or heat source at i-node, respectivelyi u,rampAnd
the maximum upward ramp rate for power or heat source power generation and heat production at the i-node; pi d,rampAnd
the maximum downward ramp rate for power or heat generation at the i-node;
(4) the rotary standby constraint considers the uncertainty of renewable energy and load, and a system needs to reserve a certain standby capacity, specifically:
in the formula, rui,t、rdi,tSpare capacity provided for the unit at the node i in the time period t; n is a radical ofresThe method comprises the steps of providing a set of nodes where units providing rotary spare capacity are located; SRu、SRdThe system is in a rotating standby requirement;
(5) the electric energy storage equipment constraint mainly comprises energy storage system SOC constraint and energy charging and discharging power constraint:
in the formula (I), the compound is shown in the specification,
and
is the maximum and minimum energy storage values of the energy storage device at the i node; pi chr,e,maxAnd Pi dis ,e,maxAnd the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
Further, the power flow constraint established in step S14 is a basic power flow calculation equation:
in the formula, Pi、QiInjecting active power and reactive power into the node i; u shapeiIs the voltage amplitude of node i; G. b is the real part and the imaginary part of the node admittance matrix; deltai,jIs the phase angle difference between node i and node j;
the established heat supply network flow constraint is a heat supply network flow calculation basic equation:
wherein the thermal mass continuous equation and the loop pressure equation are respectively as follows:
in the formula (I), the compound is shown in the specification,
the mass flow rate (kg/s) injected into node i for time t;
the heat mass flow rate in the heat supply and heat return pipeline l is set for t period;
respectively injecting a set of heat supply pipelines and a set of heat return pipelines of the node i and the outflow node i at the time t;
heat supply and heat return pipelines along the j direction of the loop and the j direction of the reverse loop at the moment t are respectively collected;
the resistance coefficient of the heat supply and heat return pipeline l at the moment t is shown, and the length, the diameter and the
(ii) related;
the node temperature equation, the pipeline temperature equation and the heat and mass mixing equation are respectively as follows:
in the formula, CpIs the specific heat capacity (C) of waterp＝4.182×10-3MJ，kg-1·℃-1)；
And
supplying heat, outlet and regenerative temperatures to a node i at the time t;
the temperatures of the inlets and the outlets of the heat supply pipeline l and the heat return pipeline l at the moment t; lambda [ alpha ]lHeat transfer coefficient per unit length of pipe (Wm)-1℃-1)；
Is the length of the pipe l;
the soil temperature outside the pipeline l is t time period.
The invention has the following advantages and beneficial effects:
the output of the comprehensive energy to each energy unit in the system is optimized through the optimized scheduling of the comprehensive energy system, and the load demand is regulated and controlled through the demand response, so that the minimum system operation cost is obtained; by considering the corresponding behavior of the load, various energy requirements of the load can correspond to the price signal of the upper layer without excessively influencing the comfort level. By fully considering the flexibility of the load side, the scheduling method can provide theoretical guidance for the optimal scheduling of the comprehensive energy system under the high level of the Internet of things.
Drawings
Fig. 1 is a basic flowchart of an optimized scheduling method of an integrated energy system in the context of an internet of things for electric power according to an embodiment of the present invention;
fig. 2 is a general flowchart of an optimized scheduling method for an integrated energy system in the context of an internet of things for electric power according to an embodiment of the present invention;
FIG. 3 is a flow chart of an optimized scheduling algorithm based on a particle swarm algorithm according to an embodiment of the present invention;
FIG. 4 is an example system topology provided by an embodiment of the present invention;
fig. 5 shows changes in load of each node, changes in average temperature of a region, and changes in fan output according to an embodiment of the present invention;
fig. 6 is a diagram illustrating a change in the degree of adaptation according to an embodiment of the present invention;
FIG. 7 illustrates a change in thermal load demand provided by an embodiment of the present invention;
fig. 8 shows the change of the scheduling cost of each time interval in two scenarios provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The comprehensive energy optimization scheduling method based on the cloud side architecture and the information acquisition characteristics in the ubiquitous power internet of things environment and the market environment is considered, and based on the frequent interaction process of the heat supply network and the power grid of the comprehensive energy system, a comprehensive energy optimization scheduling model considering source-load interaction and multi-energy complementation is provided and comprises an objective function and corresponding operation constraints, wherein the objective function is aimed at the optimal economic operation of the comprehensive energy system. The objective functions comprise the running cost of energy equipment such as a cogeneration unit, a single generator set, a heat source and the like, demand response compensation paid to a load, wind and light abandoning cost and electricity purchasing cost to a large power grid. The operation constraints include thermoelectric power flow constraints, generator set and heat source operation constraints, rotational standby constraints, electrical energy storage device constraints, thermal load in-building temperature constraints, and load demand response constraints.
Aiming at the strong nonlinear characteristic in the scheduling process, the invention adopts an APSO algorithm to solve the optimized scheduling model of the comprehensive energy system, the scheduling variables of the algorithm are the output of each energy device and the price signal of the comprehensive demand response, and the signal of the comprehensive demand response is transmitted to the load side after the particle initialization. The load side considers the energy utilization characteristics, the energy requirements and the types and characteristics of schedulable equipment of the user, coordinates the output of the equipment on the load side and the load requirements by combining the price signal of the scheduling center, and transmits the energy utilization requirements of the responded load on the comprehensive energy network to the upper scheduling center. The method comprises the steps that after a dispatching center obtains energy consumption requirements of a load side, load flow calculation is carried out on an integrated energy system by combining output of initialized energy equipment and a topological structure and basic parameters of an integrated energy network, the operation cost of the integrated energy system is obtained, particle individual and global fitness values are updated according to an APSO algorithm, if the maximum iteration times are not reached, the positions of particles are updated to obtain new dispatching variables, load side dispatching is carried out again according to the new dispatching variables, the dispatching fitness values are obtained according to the results of load side demand response, iteration is carried out in a circulating mode until iteration is stopped, and dispatching results are output. The method comprises the following specific steps.
S11: establishing a key equipment model and a load demand response model of the comprehensive energy system, wherein the key equipment comprises an electric energy storage equipment model and a cogeneration unit model, and the load demand response model comprises two demand response models of a heat load and an electric load;
s12: establishing a comprehensive energy system network model, wherein the comprehensive energy network comprises a power network and a heat network, and analyzing energy flow rules in different networks;
s13: establishing an optimized dispatching model of an objective function with the lowest operation cost of the comprehensive energy system as a target, wherein the operation cost of equipment is calculated according to the key equipment model in the step S11, and the income of selling electricity and selling heat is calculated through the load demand response model in the step S11;
s14: constraining the optimized operation process of the comprehensive energy system, establishing temperature constraint, load demand response constraint, generator set and heat source operation constraint, rotation standby constraint and electric energy storage equipment constraint in a heat load building according to the key equipment and load demand response model in the step S11, and establishing thermoelectric power flow constraint and power grid power flow constraint according to the comprehensive energy system network model in the step S12;
s15: and solving the optimized dispatching model based on an improved particle swarm algorithm, dividing the optimized dispatching process into an upper layer and a lower layer, wherein the upper layer is the optimized dispatching model of the comprehensive energy system in the step S13 and the step S14, the lower layer is the process of solving the energy demand of the user through the load side demand response model in the step S11, the process of calculating the objective function in the step S13 is carried out, and the optimal day-ahead dispatching result is obtained through continuous interaction of the two layers.
Step S11, the key equipment model of the integrated energy system includes a cogeneration unit model and an energy storage equipment model, which are specifically as follows:
the cogeneration unit model is as follows:
when the cogeneration unit works in a constant output state, the thermoelectric relationship curve is expressed as follows:
wherein Z is a coefficient describing the relationship between the heat production and the electricity production of the turboset CHP1, etaeThe electric energy conversion efficiency of the unit under the maximum continuous working condition full-condensation mode is obtained; fin(MW) is the turboset fuel consumption rate;
when the cogeneration unit works in a variable output state, the thermoelectric relationship curve is represented as:
in the formula, cmIs made into a machineThe thermoelectric ratio of the stack.
The electrical energy storage device model is as follows:
in the formula (I), the compound is shown in the specification,
the SOC value of the electric energy storage equipment at the node i in the period t;
an energy storage efficiency for the energy storage device;
charging efficiency for the energy storage device;
and
the charging and discharging power of the energy storage equipment;
the thermal energy storage device model is as follows:
in the formula (II)
The SOC value of the heat storage boiler at the node i in the t period is obtained;
the energy storage efficiency of the heat storage boiler is improved;
the energy charging efficiency of the heat storage boiler is improved;
and
the energy charging and discharging power of the heat storage boiler.
The load demand response model established in step S11 includes a thermal load corresponding model and an electrical load corresponding model, which are specifically described as follows:
the thermal load can be increased or decreased without excessively affecting the indoor temperature, but is generally not transferred to other periods, and is generally expressed by a self-elastic coefficient:
in the formula (I), the compound is shown in the specification,
the coefficient of self-elasticity for the thermal load at node i during time t;
the heat supply price in the t period;
the thermal load at node i is t periods before the demand response;
a heat load demand response price for a period of t;
is the amount of change in thermal load after demand response;
the thermal load change is represented by the following formula:
in the formula (I), the compound is shown in the specification,
the thermal load at node i for time period t;
part of the electrical load can be transferred to other time periods, and part of the electrical load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are commonly expressed as follows:
in the formula (I), the compound is shown in the specification,
the self-elastic coefficient of the electrical load at the node i in the period t;
electricity prices for a period of t;
an electrical load at node i for a period t before a demand response;
responding price for electric load demand in t period;
the variation of the electrical load at the node i in the period t of the demand response;
the mutual elastic coefficient of the electrical load at the node i in the t period to the tau period;
the electrical load variation can be represented by the following equation:
in the formula (I), the compound is shown in the specification,
the electrical load at node i is time period t.
The integrated energy system network model of step S12 is as follows:
the power grid load flow calculation formula is as follows:
in the formula, Pi、QiInjecting active power and reactive power into the node i; u shapeiIs the voltage amplitude of node i; G. b is the real part and the imaginary part of the node admittance matrix; deltai,jIs the phase angle difference between node i and node j.
The flow calculation formula of the heat supply network is as follows:
the thermal mass continuous equation and the loop pressure equation are respectively
In the formula (I), the compound is shown in the specification,
the mass flow rate (kg/s) injected into node i for time t;
the heat mass flow rate in the heat supply and heat return pipeline l is set for t period;
respectively injecting a set of heat supply pipelines and a set of heat return pipelines of the node i and the outflow node i at the time t;
heat supply and heat return pipelines along the j direction of the loop and the j direction of the reverse loop at the moment t are respectively collected;
the resistance coefficient of the heat supply and heat return pipeline l at the moment t is shown, and the length, the diameter and the
To a
The node temperature equation, the pipeline temperature equation and the heat and mass mixed equation are
In the formula, CpIs the specific heat capacity (C) of waterp＝4.182×10-3MJ，kg-1·℃-1)；
And
for the heating temperature and outlet temperature of the t-time node iAnd a heat regeneration temperature;
and
the temperatures of the inlets and the outlets of the heat supply pipeline l and the heat return pipeline l at the moment t; lambda [ alpha ]lHeat transfer coefficient per unit length of pipe (Wm)-1℃-1)；
Is the length of the pipe l;
the soil temperature outside the pipeline l is t time period.
The step S13 is based on an algorithm, and aims to define a control target of optimal scheduling and implement quantitative evaluation on an optimal scheduling effect. Specifically, the operation optimization requirements of a dispatching side and a load side of the comprehensive energy system are analyzed, and a comprehensive energy system objective function is established:
in the formula, CupperA scheduling cost for the scheduling; t is tdIs the number of scheduling periods; n is a radical ofsourceThe method comprises the steps of collecting all energy equipment nodes of a system; n is a radical ofLIs a load node set; n is a radical ofreThe method comprises the steps of (1) setting a renewable energy source electric field node set;
operating cost of energy equipment such as a cogeneration unit, a single generator set, a heat source and the like at the ith node in a time period t; (ii) a
Demand response compensation paid to the i-node load for the dispatch center at time t;
the cost of abandoning the wind and light for the node i at the time t,
the electricity purchasing cost from the regional comprehensive energy network to the superior power grid is reduced.
The cost of the generator set or heat source may be represented by a secondary model:
in the formula (I), the compound is shown in the specification,
the operating cost of the generator set or the heat source at the node i in the time period t;
and
the electric output and the thermal output of the generator set or the heat source at the i node; c. C0,i、c1,i、c2,i、c3,i、c4,iAnd c5,iThe coefficient is calculated for the cost and is determined by the condition of the unit.
The load demand response compensation may be expressed as:
in the formula (I), the compound is shown in the specification,
the price is compensated for the electricity and heat demand response, and the load side can be adjusted by adjusting the compensated price.
The wind curtailment cost may be expressed as:
in the formula, ρabanPunishment price for wind and light abandonment,
the wind and light abandoning amount of the renewable energy unit.
The electricity purchase cost to the upper-level grid can be expressed as:
in the formula (I), the compound is shown in the specification,
for the purchase price, P, of the system to the superordinate grid during the period tt buy,upperAnd the system purchases the electric quantity from the superior power grid for the time period t.
The step S14 can ensure that the optimal scheduling maximizes the objective function and simultaneously prevents the occurrence of safety problems such as line overload, etc., thereby maintaining the normal operation of the market environment. Specifically, the operation characteristics of the dispatching side and the load side of the integrated energy system are analyzed, and the optimized dispatching operation constraint of the integrated energy system is established, specifically, the power flow equation of the power grid and the heat supply network in the step S12
The calculation formula of the operation constraint of the generator set and the heat source is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the output of a power supply or a heat source at a node i in the t time period; pi min，Pi max，
Maximum minimum generated heat production power, P, of power or heat source at i-node, respectivelyi u,rampAnd
the maximum upward ramp rate for power or heat source power generation and heat production at the i-node; pi d,rampAnd
the maximum downward ramp rate for power or heat generation at the i-node.
The rotating standby constraint calculation formula is as follows:
in the formula, rui,t、rdi,tSpare capacity provided for the unit at the node i in the time period t; n is a radical ofresThe method comprises the steps of providing a set of nodes where units providing rotary spare capacity are located; SRu、SRdAnd the system is rotated for standby.
The electrical energy storage device constraint calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
and
is the maximum and minimum energy storage values of the energy storage device at the i node; pi chr,e,maxAnd Pi dis ,e,maxAnd the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
The temperature constraint calculation formula in the thermal load building is as follows:
in the formula, Ti bui,uAnd Ti bui,dRespectively an upper limit and a lower limit of the indoor temperature after heat supply.
The demand response constraint calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively responding to the load participation coefficients of the thermoelectric demand of the load at the node i;
the load at the i node is the time period of the thermal energy and the electric energy demand before the demand response.
In the step S15, the process of solving the optimized scheduling model includes the following steps:
s21: initializing parameters, and setting the maximum iteration times and the learning rate;
s22: performing output initialization and demand response price initialization on the cogeneration unit;
s23: setting the iteration number g to be 1;
s24: acquiring the response of the load side to the price;
s25: uploading the load side information to a power grid dispatching center;
s26: solving an optimized scheduling problem;
s27: and outputting the scheduling cost and the operation curve.
The step S21 is to set initial parameters of the algorithm, and the size of the parameters can directly influence the optimization speed of the algorithm, whether convergence is possible, whether the algorithm is prone to fall into the locally optimal solution, and other characteristics
The step S22 is an initialization process for optimizing the scheduling control variables.
The step S23 is a flag indicating the start of the actual optimization process.
The steps S24 and S25 are an interactive process between upper-layer optimization and lower-layer optimization, wherein the upper-layer optimization realizes indirect influence on the lower-layer optimization through demand response electricity price, and the economic operation of the whole system is ensured; the lower layer optimization assists the decision of the upper layer dispatching center by uploading actual energy demand, and meanwhile, response to the upper layer control signal is achieved on the premise of guaranteeing the self energy demand of the user.
And step S26, optimizing and scheduling a specific process for the comprehensive energy system. The input information is various kinds of schedulable device information and the load side actual energy use information obtained in the step S23; and further carrying out optimization scheduling based on comprehensive energy flow calculation, determining the output of various energy devices, updating the positions of particles, obtaining a new demand response price, transmitting the price to the load side again, and enabling the load to respond until the load tends to be optimal. The specific solving process is shown in fig. 3.
Example analysis
The calculation system of the invention is shown in fig. 4, wherein the power grid is an improved IEEE33 node, node 0 is connected with an external large power grid, a distributed cogeneration unit is added at nodes 24 and 32 respectively, the distributed cogeneration unit is coupled with a heat supply network, and a fan unit is connected at node 31; the output of the CHP1 unit is adjusted quickly, but the unit cost is high, and the CHP1 unit is mainly used for peak shaving; the CHP2 unit has low unit cost, and is mainly used for power generation and heat production. The heat supply network in the system is a 13-node heat supply network, wherein nodes 2, 3, 4, 6, 7, 8, 9 and 10 are load nodes, distributed electric boilers are arranged at the load nodes, interaction between the power grid and the heat supply network can be realized, a fixed heat source is arranged at the node 12, and output force is unchanged. In fig. 4, the nodes in the same dashed box are regarded as being located in the same region, and the distributed electric boiler at the thermal load node is supplied with power by the grid node in the same region, and is regarded as a load for the grid node. The load of each node of the heat supply network, the external temperature, the fan output and the load of each node of the power grid are shown in FIG. 5; the correlation coefficients of each heat source and cogeneration unit are shown in table 1.
Value of correlation of each energy equipment
The global optimal fitness change process of the APSO algorithm and the traditional PSO algorithm in the iterative optimization process is shown in FIG. 6. In the initial stage of iteration, compared with the traditional PSO algorithm, the APSO algorithm has no great advantage in convergence speed and even has slower speed, which is mainly because the inertia weight of particles in the initial stage of iteration is larger and larger variation is easier to generate, so that the particle distribution is more dispersed; with the increase of the iteration times, the particle convergence speed is higher, and the optimal solution is found earlier.
The gray area in fig. 7 is the reduction amount of the heat energy demand of the user in each time period after the comprehensive demand response, as can be seen from fig. 7, the reduction of the heat load is mainly in the time period with more wind curtailment at night, but considering that the system still has more wind curtailment in the present scene, it can be expected that if the heat load is further reduced, but the influence on the comfort level of the user is caused by the reduction of the load, the further stimulation of the reduction of the heat load through the price signal will generate higher cost, and the continuous increase of the system through the demand response is not economical for the consumption of the wind power.
Fig. 8 is a graph of the change of the scheduling cost in each period, and it can be seen from fig. 8 that the penalty price of wind curtailment is higher to promote the consumption of green clean energy, so that the overall operating cost of the system is obviously higher in the period of generating more wind curtailment than the period of no wind curtailment despite the higher level of the power load in the daytime. The thermoelectric comprehensive scheduling method can obviously reduce the generation of the abandoned wind, so the operation cost of the system can be greatly reduced from night to early morning.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. An optimized scheduling method for a comprehensive energy system under the background of an electric power Internet of things is characterized by comprising the following steps:
s11: establishing a key equipment model and a load demand response model of the comprehensive energy system, wherein the key equipment comprises an electric energy storage equipment model and a cogeneration unit model, and the load demand response model comprises two demand response models of a heat load and an electric load;
s12: establishing a comprehensive energy system network model, wherein the comprehensive energy network comprises a power network and a heat network, and analyzing energy flow rules in different networks;
s13: establishing an optimized dispatching model of an objective function with the lowest operation cost of the comprehensive energy system as a target, wherein the operation cost of equipment is calculated according to the key equipment model in the step S11, and the income of selling electricity and selling heat is calculated through the load demand response model in the step S11;
s14: constraining the optimized operation process of the comprehensive energy system, establishing temperature constraint, load demand response constraint, generator set and heat source operation constraint, rotation standby constraint and electric energy storage equipment constraint in a heat load building according to the key equipment and load demand response model in the step S11, and establishing thermoelectric power flow constraint and power grid power flow constraint according to the comprehensive energy system network model in the step S12;
s15: and solving the optimized dispatching model based on an improved particle swarm algorithm, dividing the optimized dispatching process into an upper layer and a lower layer, wherein the upper layer is the optimized dispatching model of the comprehensive energy system in the step S13 and the step S14, the lower layer is the process of solving the energy demand of the user through the load side demand response model in the step S11, the process of calculating the objective function in the step S13 is carried out, and the optimal day-ahead dispatching result is obtained through continuous interaction of the two layers.
2. The method for optimal scheduling of an integrated energy system in the context of the internet of things of electric power of claim 1, wherein the key equipment model of the integrated energy system established in step S11 is as follows:
when the cogeneration unit operates in the maximum output state, the relationship curve is expressed as follows:
wherein Z is a coefficient describing the relationship between the heat production and the electricity production of the turboset CHP1, etaeThe electric energy conversion efficiency of the unit under the maximum continuous working condition full-condensation mode is obtained; fin(MW) is the turboset fuel consumption rate;
when the cogeneration unit works in a variable output state, the relationship curve is expressed as follows:
in the formula, cmIs the thermoelectric ratio of the unit.
3. The method for optimal scheduling of an integrated energy system in the context of the internet of things of electric power of claim 1, wherein the key equipment model of the integrated energy system established in step S11 is as follows:
in the formula (I), the compound is shown in the specification,
the SOC value of the electric energy storage equipment at the node i in the period t;
an energy storage efficiency for the energy storage device;
charging efficiency for the energy storage device;
and
the charging and discharging power of the energy storage equipment;
the heat supply network generally stores energy through a heat storage boiler, converts electric energy into heat energy for storage in a time period with low electricity price, and releases the heat energy in a time period with high heat demand, and the SOC of the heat supply network is calculated by the following formula:
in the formula (II)
The SOC value of the heat storage boiler at the node i in the t period is obtained;
the energy storage efficiency of the heat storage boiler is improved;
the energy charging efficiency of the heat storage boiler is improved;
and
the energy charging and discharging power of the heat storage boiler.
4. The method for optimizing and scheduling a comprehensive energy system in the context of the internet of things of electric power of claim 1, wherein the load demand response model established in step S11 is represented by a self-elastic coefficient:
in the formula (I), the compound is shown in the specification,
the coefficient of self-elasticity for the thermal load at node i during time t;
the heat supply price in the t period;
the thermal load at node i is t periods before the demand response;
a heat load demand response price for a period of t;
is the amount of change in thermal load after demand response;
the thermal load change is represented by the following formula:
in the formula (I), the compound is shown in the specification,
the thermal load at node i for time period t;
part of the electrical load can be transferred to other time periods, and part of the electrical load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are commonly expressed as follows:
in the formula (I), the compound is shown in the specification,
the self-elastic coefficient of the electrical load at the node i in the period t;
electricity prices for a period of t;
an electrical load at node i for a period t before a demand response;
responding price for electric load demand in t period;
the variation of the electrical load at the node i in the period t of the demand response;
the mutual elastic coefficient of the electrical load at the node i in the t period to the tau period;
the electrical load change is represented by the following formula:
in the formula (I), the compound is shown in the specification,
the electrical load at node i is time period t.
5. The method for optimizing and scheduling an integrated energy system in the context of the internet of things of electric power according to claim 1, wherein the objective function calculation formula established in the step S13 is:
in the formula, CupperA scheduling cost for the scheduling; t is tdIs the number of scheduling periods; n is a radical ofsourceThe method comprises the steps of collecting all energy equipment nodes of a system; n is a radical ofLIs a load node set; n is a radical ofreThe method comprises the steps of (1) setting a renewable energy source electric field node set;
operating cost of energy equipment such as a cogeneration unit, a single generator set, a heat source and the like at the ith node in a time period t;
demand response compensation paid to the i-node load for the dispatch center at time t;
the cost of abandoning the wind and light for the node i at the time t,
the electricity purchasing cost from the regional comprehensive energy network to a superior power grid;
the cost of the generator set or heat source is represented by a quadratic model:
in the formula (I), the compound is shown in the specification,
the operating cost of the generator set or the heat source at the node i in the time period t;
and
solving the electric output and the thermal output of the generator set or the heat source at the node i through the key equipment model in the step S11 and the comprehensive energy system network model in the step S12; c. C0,i、c1,i、c2,i、c3,i、c4,iAnd c5,iCalculating a coefficient for the cost, which is determined by the self condition of the unit;
the demand response compensation for load payments is expressed as:
in the formula (I), the compound is shown in the specification,
the price is compensated for the electricity and heat demand response, and the adjustment on the load side can be realized by adjusting the compensation price; the variation of the electric energy demand and the heat energy demand is solved through a load demand response model in the step S11;
wind and light rejection costs are expressed as:
in the formula, ρabanPunishment price for wind and light abandonment,
the wind and light abandoning amount of the renewable energy source unit is reduced;
the cost of electricity purchase to the upper-level grid can be expressed as:
in the formula (I), the compound is shown in the specification,
for the purchase price, P, of the system to the superordinate grid during the period tt buy,upperAnd the system purchases the electric quantity from the superior power grid for the time period t.
6. The method for optimal scheduling of the integrated energy system in the context of the internet of things of electric power of claim 1, wherein the step S14 is performed
(1) The temperature constraint in the thermal load building is expressed on the premise that the temperature in the building is not influenced excessively and is represented as follows:
in the formula, Ti bui,uAnd Ti bui,dRespectively an upper limit and a lower limit of the indoor temperature after heat supply;
(2) the load demand response constraint is that the demand response of the load to the price signal has a certain limit, which is specifically expressed as:
in the formula (I), the compound is shown in the specification,
and
thermoelectric demand response for loads at node i, respectivelyA load participation coefficient;
the load at the i node is required for the heat energy and the electric energy at the t time period before the demand response;
(3) the output range of the generator set and the heat source is certain in the operation restriction of the generator set and the heat source, and meanwhile, the power climbing restriction of the generator is also considered, and the method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
and
respectively the output of a power supply or a heat source at a node i in the t time period; pi min，Pi max，
Are respectively an i nodeMaximum minimum generated heat production power, P, of the power or heat sourcei u,rampAnd
the maximum upward ramp rate for power or heat source power generation and heat production at the i-node; pi d,rampAnd
the maximum downward ramp rate for power or heat generation at the i-node;
(4) the rotary standby constraint considers the uncertainty of renewable energy and load, and a system needs to reserve a certain standby capacity, specifically:
in the formula, rui,t、rdi,tSpare capacity provided for the unit at the node i in the time period t; n is a radical ofresThe method comprises the steps of providing a set of nodes where units providing rotary spare capacity are located; SRu、SRdThe system is in a rotating standby requirement;
(5) the electric energy storage equipment constraint mainly comprises energy storage system SOC constraint and energy charging and discharging power constraint:
in the formula (I), the compound is shown in the specification,
and
is the maximum and minimum energy storage values of the energy storage device at the i node; pi chr,e,maxAnd Pi dis,e,maxAnd the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
7. The method for optimizing and scheduling the comprehensive energy system in the context of the internet of things of electric power of claim 1, wherein the power grid power flow constraint established in the step S14 is a power flow calculation basic equation:
in the formula, Pi、QiInjecting active power and reactive power into the node i; u shapeiIs the voltage amplitude of node i; G. b is the real part and the imaginary part of the node admittance matrix; deltai,jIs the phase angle difference between node i and node j;
the established heat supply network flow constraint is a heat supply network flow calculation basic equation:
wherein the thermal mass continuous equation and the loop pressure equation are respectively as follows:
in the formula (I), the compound is shown in the specification,
the mass flow rate (kg/s) injected into node i for time t;
the heat mass flow rate in the heat supply and heat return pipeline l is set for t period;
respectively injecting a set of heat supply pipelines and a set of heat return pipelines of the node i and the outflow node i at the time t;
heat supply and heat return pipelines along the j direction of the loop and the j direction of the reverse loop at the moment t are respectively collected;
the resistance coefficient of the heat supply and heat return pipeline l at the moment t is shown, and the length, the diameter and the
(ii) related;
the node temperature equation, the pipeline temperature equation and the heat and mass mixing equation are respectively as follows:
in the formula, CpIs the specific heat capacity (C) of waterp＝4.182×10-3MJ，kg-1·℃-1)；
And
supplying heat, outlet and regenerative temperatures to a node i at the time t;
and
the temperatures of the inlets and the outlets of the heat supply pipeline l and the heat return pipeline l at the moment t; lambda [ alpha ]lHeat transfer coefficient per unit length of pipe (Wm)-1℃-1)；
Is the length of the pipe l;
the soil temperature outside the pipeline l is t time period.
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